Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 65982

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 72371

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-22T20:51:43+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

COVID-19 対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	134857
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	133035	0
dateAnnounced	0	1.00	10	10	0	299	0
gender	32557	0.76	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	130745	0.03	8	23	0	8	0
notes	72209	0.46	1	270	0	59718	1
mhlwPatientNumber	134408	0.00	1	11	0	434	0
prefecturePatientNumber	28873	0.79	5	20	0	105975	0
prefectureSourceURL	103466	0.23	5	224	0	3453	0
residence	40850	0.70	1	38	0	1427	0
sourceURL	1170	0.99	1	239	0	8619	0
relatedPatients	123182	0.09	2	259	0	6967	0
knownCluster	132331	0.02	3	88	0	234	0
detectedCityTown	107010	0.21	2	22	0	667	0
cityPrefectureNumber	107309	0.20	1	34	0	27539	2
citySourceURL	122839	0.09	9	317	0	3673	0
deceasedDate	132904	0.01	10	10	0	249	0
deceasedReportedDate	133637	0.01	10	62	0	207	0
deathSourceURL	133782	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 133034, FAL: 1823
charterFlightPassenger	134843	0	1.00	TRU: 14
cruisePassengerDisembarked	134846	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	28.57	24.6	-1	0	20	50	100	▇▇▅▃▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	131593
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	131593	0
knownCluster	129117	0.02	3	88	0	231	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-22	2020-08-29	296
deceasedDate	131214	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	131264	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	30885	0.77	FALSE	2	M: 56429, F: 44279
patientStatus	129082	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 30970, 20: 27222, 30: 17469, 40: 14655
pcode	0	1.00	FALSE	47	13: 37746, 27: 17649, 14: 11362, 23: 8830
pref	0	1.00	FALSE	47	東京都: 37746, 大阪府: 17649, 神奈川: 11362, 愛知県: 8830
region	0	1.00	FALSE	8	関東地: 65982, 近畿地: 27742, 中部地: 13776, 九州地: 12306
広域圏	11156	0.92	FALSE	8	首都圏: 66302, 近畿圏: 27022, 中部圏: 12293, 九州圏: 8302
通俗的区分	0	1.00	FALSE	11	関東: 65982, 関西: 27022, 東海: 11688, 九州: 8302
fct_pref	0	1.00	FALSE	47	Tok: 37746, Osa: 17649, Kan: 11362, Aic: 8830

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 131593
charterFlightPassenger	131586	0	1.00	TRU: 7
cruisePassengerDisembarked	131582	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 129117, TRU: 2476

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.04）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.04）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 211.9755 246.8618 266.4639 283.0634 297.0029 271.5139 280.5406 275.6423
##  [9] 294.2515 303.3103 310.4506 314.3460 314.5467 320.3906
## 
## $北海道地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 200.2706 194.0745
## 45.85714 233.0821 225.7877
## 46.00000 249.0574 239.8430
## 46.14286 264.8494 255.2074
## 46.28571 277.8177 267.6617
## 46.42857 251.2292 240.4911
## 46.57143 258.7881 247.2730
## 46.71429 250.2177 236.7588
## 46.85714 266.3828 251.6300
## 47.00000 272.6457 256.4129
## 47.14286 277.9856 260.7997
## 47.28571 279.9432 261.7315
## 47.42857 278.0902 258.7913
## 47.57143 281.6620 261.1604
## 
## $北海道地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 223.6804 229.8766
## 45.85714 260.6414 267.9359
## 46.00000 283.8704 293.0848
## 46.14286 301.2775 310.9194
## 46.28571 316.1881 326.3441
## 46.42857 291.7987 302.5368
## 46.57143 302.2931 313.8082
## 46.71429 301.0668 314.5257
## 46.85714 322.1202 336.8730
## 47.00000 333.9748 350.2077
## 47.14286 342.9155 360.1014
## 47.28571 348.7487 366.9605
## 47.42857 351.0032 370.3021
## 47.57143 359.1192 379.6208
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 30.94984 36.00383 33.97818 32.27436 34.16675 35.22917 31.81197 33.86993
##  [9] 33.28516 34.15149 33.54178 34.25954 33.88233 34.34472
## 
## $東北地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 24.05130 20.39943
## 45.85714 28.77476 24.94793
## 46.00000 26.67360 22.80679
## 46.14286 24.94354 21.06283
## 46.28571 26.59578 22.58796
## 46.42857 27.65332 23.64291
## 46.57143 23.99809 19.86167
## 46.71429 25.66310 21.31866
## 46.85714 24.76164 20.24956
## 47.00000 25.56487 21.01939
## 47.14286 24.81234 20.19126
## 47.28571 25.39881 20.70822
## 47.42857 24.92837 20.18843
## 47.57143 25.23848 20.41793
## 
## $東北地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 37.84838 41.50025
## 45.85714 43.23289 47.05972
## 46.00000 41.28276 45.14957
## 46.14286 39.60518 43.48589
## 46.28571 41.73772 45.74554
## 46.42857 42.80503 46.81544
## 46.57143 39.62585 43.76227
## 46.71429 42.07677 46.42120
## 46.85714 41.80868 46.32076
## 47.00000 42.73810 47.28359
## 47.14286 42.27121 46.89230
## 47.28571 43.12026 47.81085
## 47.42857 42.83629 47.57624
## 47.57143 43.45095 48.27150
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1]  674.1798  917.4331 1039.9305 1126.3255 1122.9201 1159.1475  956.2292
##  [8]  807.8860 1034.1801 1153.4008 1228.2649 1222.1495 1258.1420 1051.3859
## 
## $関東地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  607.5201  572.2326
## 45.85714  831.7208  786.3474
## 46.00000  944.3583  893.7654
## 46.14286 1023.9308  969.7263
## 46.28571 1015.4694  958.5884
## 46.42857 1049.2330  991.0478
## 46.57143  840.6180  779.4172
## 46.71429  678.6938  610.3035
## 46.85714  892.5805  817.6223
## 47.00000 1003.6296  924.3455
## 47.14286 1072.0239  989.3149
## 47.28571 1059.6946  973.6962
## 47.42857 1091.6962 1003.5851
## 47.57143  879.1234  787.9332
## 
## $関東地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  740.8395  776.1269
## 45.85714 1003.1454 1048.5188
## 46.00000 1135.5028 1186.0957
## 46.14286 1228.7202 1282.9247
## 46.28571 1230.3708 1287.2517
## 46.42857 1269.0620 1327.2472
## 46.57143 1071.8403 1133.0411
## 46.71429  937.0783 1005.4685
## 46.85714 1175.7796 1250.7379
## 47.00000 1303.1721 1382.4562
## 47.14286 1384.5059 1467.2149
## 47.28571 1384.6044 1470.6028
## 47.42857 1424.5878 1512.6989
## 47.57143 1223.6483 1314.8385
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 215.0489 275.1975 331.1105 342.5339 335.2126 344.0632 287.2199 285.4019
##  [9] 306.5247 323.6692 329.8864 326.8901 334.0951 315.5507
## 
## $中部地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 192.6037 180.7219
## 45.85714 246.9829 232.0471
## 46.00000 298.8215 281.7287
## 46.14286 307.5685 289.0589
## 46.28571 297.6766 277.8063
## 46.42857 304.5582 283.6456
## 46.57143 245.6244 223.6051
## 46.71429 236.7307 210.9657
## 46.85714 252.7215 224.2398
## 47.00000 265.8411 235.2288
## 47.14286 268.4704 235.9587
## 47.28571 262.3092 228.1222
## 47.42857 266.4878 230.6986
## 47.57143 245.1592 207.8962
## 
## $中部地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 237.4941 249.3758
## 45.85714 303.4120 318.3478
## 46.00000 363.3995 380.4923
## 46.14286 377.4993 396.0089
## 46.28571 372.7485 392.6189
## 46.42857 383.5681 404.4808
## 46.57143 328.8155 350.8348
## 46.71429 334.0731 359.8380
## 46.85714 360.3279 388.8096
## 47.00000 381.4973 412.1096
## 47.14286 391.3024 423.8141
## 47.28571 391.4709 425.6580
## 47.42857 401.7025 437.4916
## 47.57143 385.9422 423.2052
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 460.1513 679.5638 698.3801 760.0975 780.4191 835.3627 854.9316 681.9357
##  [9] 847.0768 861.2390 907.6907 922.9858 964.3391 979.0677
## 
## $近畿地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 421.3389 400.7929
## 45.85714 634.6184 610.8257
## 46.00000 649.8291 624.1277
## 46.14286 708.1907 680.7129
## 46.28571 725.3607 696.2145
## 46.42857 777.3235 746.5995
## 46.57143 794.0575 761.8327
## 46.71429 604.6774 563.7794
## 46.85714 762.0865 717.0953
## 47.00000 770.3604 722.2523
## 47.14286 811.2830 760.2478
## 47.28571 821.3492 767.5461
## 47.42857 857.7299 801.2944
## 47.57143 867.7076 808.7571
## 
## $近畿地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  498.9638  519.5098
## 45.85714  724.5092  748.3019
## 46.00000  746.9312  772.6325
## 46.14286  812.0044  839.4822
## 46.28571  835.4776  864.6238
## 46.42857  893.4019  924.1260
## 46.57143  915.8058  948.0306
## 46.71429  759.1940  800.0920
## 46.85714  932.0672  977.0584
## 47.00000  952.1175 1000.2257
## 47.14286 1004.0984 1055.1336
## 47.28571 1024.6223 1078.4255
## 47.42857 1070.9484 1127.3839
## 47.57143 1090.4278 1149.3783
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 34.39466 35.07200 35.07200 35.07200 35.07200 35.07200 35.07200 35.07200
##  [9] 35.07200 35.07200 35.07200 35.07200 35.07200 35.07200
## 
## $中国地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 24.92285 19.90877
## 45.85714 25.03096 19.71556
## 46.00000 24.72331 19.24505
## 46.14286 24.42455 18.78813
## 46.28571 24.13394 18.34368
## 46.42857 23.85085 17.91074
## 46.57143 23.57474 17.48846
## 46.71429 23.30510 17.07608
## 46.85714 23.04150 16.67294
## 47.00000 22.78356 16.27845
## 47.14286 22.53092 15.89207
## 47.28571 22.28327 15.51332
## 47.42857 22.04032 15.14177
## 47.57143 21.80183 14.77702
## 
## $中国地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 43.86648 48.88056
## 45.85714 45.11303 50.42843
## 46.00000 45.42068 50.89894
## 46.14286 45.71945 51.35586
## 46.28571 46.01005 51.80031
## 46.42857 46.29314 52.23325
## 46.57143 46.56925 52.65554
## 46.71429 46.83889 53.06791
## 46.85714 47.10249 53.47105
## 47.00000 47.36044 53.86554
## 47.14286 47.61308 54.25192
## 47.28571 47.86073 54.63067
## 47.42857 48.10367 55.00222
## 47.57143 48.34217 55.36697
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 19.16939 18.34234 18.34234 18.34234 18.34234 18.34234 18.34234 18.34234
##  [9] 18.34234 18.34234 18.34234 18.34234 18.34234 18.34234
## 
## $四国地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 15.43585 13.459430
## 45.85714 14.15701 11.941423
## 46.00000 13.94779 11.621459
## 46.14286 13.74810 11.316050
## 46.28571 13.55673 11.023375
## 46.42857 13.37272 10.741962
## 46.57143 13.19529 10.470603
## 46.71429 13.02377 10.208291
## 46.85714 12.85762  9.954178
## 47.00000 12.69635  9.707541
## 47.14286 12.53956  9.467755
## 47.28571 12.38690  9.234280
## 47.42857 12.23806  9.006642
## 47.57143 12.09276  8.784424
## 
## $四国地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 22.90293 24.87935
## 45.85714 22.52768 24.74326
## 46.00000 22.73689 25.06323
## 46.14286 22.93659 25.36864
## 46.28571 23.12796 25.66131
## 46.42857 23.31197 25.94273
## 46.57143 23.48940 26.21408
## 46.71429 23.66091 26.47640
## 46.85714 23.82707 26.73051
## 47.00000 23.98834 26.97715
## 47.14286 24.14513 27.21693
## 47.28571 24.29779 27.45041
## 47.42857 24.44663 27.67805
## 47.57143 24.59193 27.90026
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1]  96.99168 101.95235 126.23446 126.09782 129.75555 126.94369 120.12485
##  [8] 118.11761 122.57269 138.26245 137.83792 141.17758 139.02427 134.15208
## 
## $九州地方$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 74.92235 63.23954
## 45.85714 75.20906 61.05201
## 46.00000 95.96578 79.94250
## 46.14286 94.46505 77.71967
## 46.28571 96.07885 78.25148
## 46.42857 89.35153 69.45145
## 46.57143 79.95177 58.68543
## 46.71429 72.38976 48.18289
## 46.85714 72.91959 46.63483
## 47.00000 84.29751 55.73021
## 47.14286 80.71532 50.47645
## 47.28571 81.24608 49.52026
## 47.42857 75.73891 42.23766
## 47.57143 67.69063 32.50807
## 
## $九州地方$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 119.0610 130.7438
## 45.85714 128.6956 142.8527
## 46.00000 156.5031 172.5264
## 46.14286 157.7306 174.4760
## 46.28571 163.4322 181.2596
## 46.42857 164.5358 184.4359
## 46.57143 160.2979 181.5643
## 46.71429 163.8455 188.0523
## 46.85714 172.2258 198.5106
## 47.00000 192.2274 220.7947
## 47.14286 194.9605 225.1994
## 47.28571 201.1091 232.8349
## 47.42857 202.3096 235.8109
## 47.57143 200.6135 235.7961

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 211.9755 246.8618 266.4639 283.0634 297.0029 271.5139 280.5406 275.6423
##  [9] 294.2515 303.3103 310.4506 314.3460 314.5467 320.3906
## 
## $北海道$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 200.2706 194.0745
## 45.85714 233.0821 225.7877
## 46.00000 249.0574 239.8430
## 46.14286 264.8494 255.2074
## 46.28571 277.8177 267.6617
## 46.42857 251.2292 240.4911
## 46.57143 258.7881 247.2730
## 46.71429 250.2177 236.7588
## 46.85714 266.3828 251.6300
## 47.00000 272.6457 256.4129
## 47.14286 277.9856 260.7997
## 47.28571 279.9432 261.7315
## 47.42857 278.0902 258.7913
## 47.57143 281.6620 261.1604
## 
## $北海道$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 223.6804 229.8766
## 45.85714 260.6414 267.9359
## 46.00000 283.8704 293.0848
## 46.14286 301.2775 310.9194
## 46.28571 316.1881 326.3441
## 46.42857 291.7987 302.5368
## 46.57143 302.2931 313.8082
## 46.71429 301.0668 314.5257
## 46.85714 322.1202 336.8730
## 47.00000 333.9748 350.2077
## 47.14286 342.9155 360.1014
## 47.28571 348.7487 366.9605
## 47.42857 351.0032 370.3021
## 47.57143 359.1192 379.6208
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.36105700 0.07894233 0.23449567 0.14872614 0.19601803 0.16994207
##  [7] 0.18431992 0.17639221 0.18076342 0.17835321 0.17968216 0.17894940
## [13] 0.17935343 0.17913065
## 
## $青森県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429 -2.168334 -3.507313
## 45.85714 -2.890411 -4.462292
## 46.00000 -2.815594 -4.430214
## 46.14286 -3.118198 -4.847603
## 46.28571 -3.195125 -4.990288
## 46.42857 -3.381015 -5.260778
## 46.57143 -3.497788 -5.446978
## 46.71429 -3.643772 -5.666045
## 46.85714 -3.766500 -5.856055
## 47.00000 -3.895309 -6.051776
## 47.14286 -4.014820 -6.235255
## 47.28571 -4.133946 -6.417055
## 47.42857 -4.248267 -6.592107
## 47.57143 -4.360587 -6.763768
## 
## $青森県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.890448 4.229427
## 45.85714 3.048296 4.620176
## 46.00000 3.284585 4.899206
## 46.14286 3.415650 5.145055
## 46.28571 3.587161 5.382324
## 46.42857 3.720899 5.600662
## 46.57143 3.866428 5.815618
## 46.71429 3.996557 6.018830
## 46.85714 4.128027 6.217582
## 47.00000 4.252015 6.408482
## 47.14286 4.374184 6.594619
## 47.28571 4.491845 6.774954
## 47.42857 4.606973 6.950814
## 47.57143 4.718848 7.122029
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1]  7.734235  9.218072 11.343911 11.115414 10.969263 10.875784 10.815992
##  [8] 10.777749 10.753288 10.737643 10.727636 10.721235 10.717141 10.714522
## 
## $岩手県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 6.420318 5.724772
## 45.85714 7.718972 6.925396
## 46.00000 9.738074 8.887994
## 46.14286 9.509547 8.659452
## 46.28571 9.335664 8.470889
## 46.42857 9.171639 8.269520
## 46.57143 9.008009 8.050920
## 46.71429 8.846188 7.823681
## 46.85714 8.688970 7.596185
## 47.00000 8.538078 7.373698
## 47.14286 8.394115 7.158824
## 47.28571 8.256989 6.952496
## 47.42857 8.126266 6.754740
## 47.57143 8.001388 6.565141
## 
## $岩手県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  9.048152  9.743697
## 45.85714 10.717172 11.510748
## 46.00000 12.949749 13.799828
## 46.14286 12.721281 13.571375
## 46.28571 12.602862 13.467637
## 46.42857 12.579928 13.482047
## 46.57143 12.623976 13.581065
## 46.71429 12.709311 13.731818
## 46.85714 12.817607 13.910391
## 47.00000 12.937208 14.101587
## 47.14286 13.061156 14.296447
## 47.28571 13.185481 14.489974
## 47.42857 13.308016 14.679542
## 47.57143 13.427657 14.863904
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 14.93663 16.24413 15.48615 14.76935 14.61520 15.23848 14.20015 15.22878
##  [9] 14.66194 15.15057 14.76827 15.03518 14.87818 14.94054
## 
## $宮城県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429  9.824984 7.119044
## 45.85714 11.034760 8.277087
## 46.00000 10.066477 7.197474
## 46.14286  9.317590 6.431603
## 46.28571  8.905531 5.883017
## 46.42857  9.501298 6.464215
## 46.57143  8.244004 5.091012
## 46.71429  9.129118 5.900154
## 46.85714  8.380874 5.055881
## 47.00000  8.769220 5.391135
## 47.14286  8.265538 4.823202
## 47.28571  8.400491 4.888298
## 47.42857  8.149291 4.587230
## 47.57143  8.069939 4.432864
## 
## $宮城県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 20.04827 22.75421
## 45.85714 21.45350 24.21117
## 46.00000 20.90583 23.77483
## 46.14286 20.22111 23.10710
## 46.28571 20.32486 23.34738
## 46.42857 20.97567 24.01275
## 46.57143 20.15629 23.30928
## 46.71429 21.32844 24.55740
## 46.85714 20.94300 24.26799
## 47.00000 21.53193 24.91001
## 47.14286 21.27099 24.71333
## 47.28571 21.66987 25.18206
## 47.42857 21.60708 25.16914
## 47.57143 21.81114 25.44821
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.3611739 0.3963243 0.3963243 0.3963243 0.3963243 0.3963243 0.3963243
##  [8] 0.3963243 0.3963243 0.3963243 0.3963243 0.3963243 0.3963243 0.3963243
## 
## $秋田県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.8641420 -1.512785
## 45.85714 -0.8409660 -1.495948
## 46.00000 -0.8423052 -1.497996
## 46.14286 -0.8436428 -1.500042
## 46.28571 -0.8449790 -1.502085
## 46.42857 -0.8463138 -1.504127
## 46.57143 -0.8476472 -1.506166
## 46.71429 -0.8489791 -1.508203
## 46.85714 -0.8503096 -1.510238
## 47.00000 -0.8516387 -1.512270
## 47.14286 -0.8529663 -1.514301
## 47.28571 -0.8542926 -1.516329
## 47.42857 -0.8556175 -1.518355
## 47.57143 -0.8569409 -1.520379
## 
## $秋田県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 1.586490 2.235133
## 45.85714 1.633615 2.288597
## 46.00000 1.634954 2.290645
## 46.14286 1.636291 2.292690
## 46.28571 1.637628 2.294734
## 46.42857 1.638962 2.296775
## 46.57143 1.640296 2.298815
## 46.71429 1.641628 2.300852
## 46.85714 1.642958 2.302886
## 47.00000 1.644287 2.304919
## 47.14286 1.645615 2.306950
## 47.28571 1.646941 2.308978
## 47.42857 1.648266 2.311004
## 47.57143 1.649590 2.313028
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.7254068 1.8504624 1.3491196 0.7507241 1.2566902 1.4443398 0.9977327
##  [8] 0.9323165 0.8284552 0.7301821 0.6385046 0.5541774 0.4777230 0.4094536
## 
## $山形県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%        95%
## 45.71429  0.7263373  0.1974619
## 45.85714  0.8359011  0.2988248
## 46.00000  0.3105198 -0.2392817
## 46.14286 -0.3193539 -0.8858190
## 46.28571  0.1496257 -0.4364189
## 46.42857  0.2969972 -0.3103694
## 46.57143 -0.1910575 -0.8203651
## 46.71429 -0.2639979 -0.8972885
## 46.85714 -0.4000159 -1.0503292
## 47.00000 -0.5260907 -1.1911214
## 47.14286 -0.6412305 -1.3186813
## 47.28571 -0.7448755 -1.4325525
## 47.42857 -0.8368266 -1.5327072
## 47.57143 -0.9171801 -1.6194575
## 
## $山形県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.724476 3.253352
## 45.85714 2.865024 3.402100
## 46.00000 2.387719 2.937521
## 46.14286 1.820802 2.387267
## 46.28571 2.363755 2.949799
## 46.42857 2.591682 3.199049
## 46.57143 2.186523 2.815831
## 46.71429 2.128631 2.761921
## 46.85714 2.056926 2.707240
## 47.00000 1.986455 2.651486
## 47.14286 1.918240 2.595690
## 47.28571 1.853230 2.540907
## 47.42857 1.792273 2.488153
## 47.57143 1.736087 2.438365
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 4.718374 3.677035 5.102054 4.200443 4.572417 4.575967 3.983607 4.264863
##  [9] 4.161146 4.215674 4.198450 4.209532 4.207031 4.209460
## 
## $福島県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%        95%
## 45.71429 2.148455  0.7880215
## 45.85714 1.037773 -0.3593684
## 46.00000 2.307576  0.8282681
## 46.14286 1.367662 -0.1319222
## 46.28571 1.701548  0.1818017
## 46.42857 1.683998  0.1530816
## 46.57143 1.073053 -0.4677013
## 46.71429 1.302223 -0.2661031
## 46.85714 1.178651 -0.4001871
## 47.00000 1.210962 -0.3796367
## 47.14286 1.177360 -0.4219092
## 47.28571 1.172510 -0.4351933
## 47.42857 1.155525 -0.4598451
## 47.57143 1.143860 -0.4789707
## 
## $福島県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 7.288294 8.648727
## 45.85714 6.316297 7.713438
## 46.00000 7.896533 9.375841
## 46.14286 7.033225 8.532809
## 46.28571 7.443285 8.963032
## 46.42857 7.467936 8.998852
## 46.57143 6.894162 8.434916
## 46.71429 7.227502 8.795829
## 46.85714 7.143642 8.722480
## 47.00000 7.220387 8.810985
## 47.14286 7.219541 8.818810
## 47.28571 7.246555 8.854258
## 47.42857 7.258537 8.873907
## 47.57143 7.275061 8.897892
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 35.19921 57.33938 56.86575 48.30189 50.96310 62.72089 56.28724 50.73820
##  [9] 59.70210 59.37676 55.87987 56.79493 61.12526 59.80587
## 
## $茨城県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 30.23436 27.60613
## 45.85714 52.09696 49.32180
## 46.00000 51.57737 48.77787
## 46.14286 42.65614 39.66746
## 46.28571 44.69560 41.37778
## 46.42857 56.22566 52.78729
## 46.57143 49.61490 46.08277
## 46.71429 43.13781 39.11440
## 46.85714 51.65139 47.38959
## 47.00000 51.07774 46.68450
## 47.14286 47.22859 42.64888
## 47.28571 47.68435 42.86150
## 47.42857 51.67820 46.67722
## 47.57143 50.07168 44.91871
## 
## $茨城県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 40.16407 42.79230
## 45.85714 62.58179 65.35696
## 46.00000 62.15413 64.95363
## 46.14286 53.94764 56.93633
## 46.28571 57.23061 60.54842
## 46.42857 69.21613 72.65449
## 46.57143 62.95959 66.49171
## 46.71429 58.33858 62.36199
## 46.85714 67.75282 72.01462
## 47.00000 67.67579 72.06902
## 47.14286 64.53115 69.11086
## 47.28571 65.90551 70.72836
## 47.42857 70.57232 75.57330
## 47.57143 69.54006 74.69303
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 6.941737 9.192329 7.411080 9.854475 8.309109 8.211247 9.577294 8.116109
##  [9] 8.400908 8.084465 8.084465 8.084465 8.084465 8.084465
## 
## $栃木県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 3.853060 2.218013
## 45.85714 6.037646 4.367658
## 46.00000 4.055673 2.279428
## 46.14286 6.450379 4.648359
## 46.28571 4.857009 3.029577
## 46.42857 4.711801 2.859307
## 46.57143 6.031135 4.153913
## 46.71429 4.352807 2.360636
## 46.85714 4.570043 2.542106
## 47.00000 4.165217 2.090492
## 47.14286 4.102996 1.995333
## 47.28571 4.041732 1.901638
## 47.42857 3.981383 1.809342
## 47.57143 3.921909 1.718384
## 
## $栃木県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 10.03042 11.66546
## 45.85714 12.34701 14.01700
## 46.00000 10.76649 12.54273
## 46.14286 13.25857 15.06059
## 46.28571 11.76121 13.58864
## 46.42857 11.71069 13.56319
## 46.57143 13.12345 15.00067
## 46.71429 11.87941 13.87158
## 46.85714 12.23177 14.25971
## 47.00000 12.00371 14.07844
## 47.14286 12.06593 14.17360
## 47.28571 12.12720 14.26729
## 47.42857 12.18755 14.35959
## 47.57143 12.24702 14.45055
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 11.810147 13.018341 12.082871 10.866484 10.674649 10.064423  9.826230
##  [8]  9.485353  9.291619  9.086351  8.946397  8.817088  8.720630  8.637141
## 
## $群馬県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%        95%
## 45.71429 7.219942  4.7900347
## 45.85714 7.644482  4.7997334
## 46.00000 6.471374  3.5008260
## 46.14286 5.185959  2.1788705
## 46.28571 4.634899  1.4376486
## 46.42857 3.932030  0.6857374
## 46.57143 3.544637  0.2193625
## 46.71429 3.128433 -0.2367168
## 46.85714 2.853404 -0.5547806
## 47.00000 2.591964 -0.8459562
## 47.14286 2.398964 -1.0670382
## 47.28571 2.226938 -1.2616769
## 47.42857 2.091331 -1.4180070
## 47.57143 1.973684 -1.5537364
## 
## $群馬県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 16.40035 18.83026
## 45.85714 18.39220 21.23695
## 46.00000 17.69437 20.66492
## 46.14286 16.54701 19.55410
## 46.28571 16.71440 19.91165
## 46.42857 16.19682 19.44311
## 46.57143 16.10782 19.43310
## 46.71429 15.84227 19.20742
## 46.85714 15.72983 19.13802
## 47.00000 15.58074 19.01866
## 47.14286 15.49383 18.95983
## 47.28571 15.40724 18.89585
## 47.42857 15.34993 18.85927
## 47.57143 15.30060 18.82802
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 122.1270 122.8792 140.7863 131.6033 117.8805 155.6403 132.9223 135.6417
##  [9] 134.0029 138.2872 138.0423 133.8847 149.8547 139.0825
## 
## $埼玉県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 107.5186  99.78547
## 45.85714 107.6121  99.53015
## 46.00000 124.8877 116.47139
## 46.14286 115.0972 106.35937
## 46.28571 100.7885  91.74062
## 46.42857 137.9820 128.63423
## 46.57143 114.7152 105.07688
## 46.71429 114.6521 103.54087
## 46.85714 112.0994 100.50448
## 47.00000 115.5066 103.44723
## 47.14286 114.4170 101.91058
## 47.28571 109.4439  96.50571
## 47.42857 124.6248 111.26885
## 47.57143 113.0874  99.32637
## 
## $埼玉県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 136.7353 144.4684
## 45.85714 138.1464 146.2283
## 46.00000 156.6850 165.1013
## 46.14286 148.1094 156.8472
## 46.28571 134.9724 144.0203
## 46.42857 173.2987 182.6464
## 46.57143 151.1294 160.7677
## 46.71429 156.6312 167.7424
## 46.85714 155.9063 167.5012
## 47.00000 161.0678 173.1272
## 47.14286 161.6676 174.1741
## 47.28571 158.3254 171.2636
## 47.42857 175.0846 188.4405
## 47.57143 165.0776 178.8386
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1]  87.82091  90.44855  89.50258  98.67813  94.40089 100.66167  91.53395
##  [8]  95.94833  95.90440  93.14921  98.88441  97.02070  98.70698  95.27255
## 
## $千葉県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 75.85617 69.52242
## 45.85714 77.22999 70.23251
## 46.00000 75.68035 68.36331
## 46.14286 84.27752 76.65429
## 46.28571 79.44424 71.52666
## 46.42857 85.16892 76.96755
## 46.57143 75.52304 67.04738
## 46.71429 78.42420 69.14749
## 46.85714 77.54093 67.81989
## 47.00000 74.09565 64.00930
## 47.14286 79.16489 68.72600
## 47.28571 76.65699 65.87709
## 47.42857 77.71884 66.60839
## 47.57143 73.67803 62.24659
## 
## $千葉県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429  99.78565 106.1194
## 45.85714 103.66711 110.6646
## 46.00000 103.32481 110.6419
## 46.14286 113.07875 120.7020
## 46.28571 109.35755 117.2751
## 46.42857 116.15442 124.3558
## 46.57143 107.54485 116.0205
## 46.71429 113.47246 122.7492
## 46.85714 114.26787 123.9889
## 47.00000 112.20277 122.2891
## 47.14286 118.60393 129.0428
## 47.28571 117.38441 128.1643
## 47.42857 119.69512 130.8056
## 47.57143 116.86706 128.2985
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 337.8146 447.7643 508.4472 574.8005 550.9392 561.9877 441.9403 386.6399
##  [9] 493.4950 553.2258 618.6468 593.8726 604.0272 483.1045
## 
## $東京都$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 289.9726 264.6466
## 45.85714 392.0382 362.5386
## 46.00000 449.1504 417.7607
## 46.14286 512.2714 479.1705
## 46.28571 485.4606 450.7984
## 46.42857 493.8009 457.7050
## 46.57143 371.2542 333.8353
## 46.71429 307.4208 265.4847
## 46.85714 409.3631 364.8263
## 47.00000 465.4826 419.0342
## 47.14286 527.5756 479.3655
## 47.28571 499.7209 449.8801
## 47.42857 507.0138 455.6581
## 47.57143 383.4245 330.6571
## 
## $東京都$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 385.6565 410.9825
## 45.85714 503.4904 532.9900
## 46.00000 567.7439 599.1337
## 46.14286 637.3296 670.4305
## 46.28571 616.4177 651.0800
## 46.42857 630.1745 666.2704
## 46.57143 512.6264 550.0453
## 46.71429 465.8591 507.7952
## 46.85714 577.6270 622.1637
## 47.00000 640.9689 687.4173
## 47.14286 709.7179 757.9281
## 47.28571 688.0242 737.8650
## 47.42857 701.0405 752.3963
## 47.57143 582.7846 635.5520
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 138.4068 190.8788 238.8468 228.8720 224.7991 213.0652 197.4303 189.0060
##  [9] 217.1560 241.6344 235.2043 231.6270 225.1723 218.1989
## 
## $神奈川県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 118.1487 107.4247
## 45.85714 167.8728 155.6942
## 46.00000 214.5331 201.6622
## 46.14286 204.2652 191.2391
## 46.28571 199.8292 186.6109
## 46.42857 187.5759 174.0826
## 46.57143 170.2135 155.8057
## 46.71429 155.7693 138.1748
## 46.85714 181.2828 162.2926
## 47.00000 204.3177 184.5634
## 47.14286 197.0487 176.8503
## 47.28571 192.5409 171.8500
## 47.42857 184.8113 163.4455
## 47.57143 175.9370 153.5649
## 
## $神奈川県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 158.6650 169.3890
## 45.85714 213.8848 226.0634
## 46.00000 263.1606 276.0315
## 46.14286 253.4788 266.5048
## 46.28571 249.7690 262.9873
## 46.42857 238.5545 252.0477
## 46.57143 224.6471 239.0549
## 46.71429 222.2428 239.8373
## 46.85714 253.0293 272.0194
## 47.00000 278.9511 298.7054
## 47.14286 273.3600 293.5584
## 47.28571 270.7131 291.4040
## 47.42857 265.5332 286.8991
## 47.57143 260.4608 282.8329
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1]  7.745488 13.867415  9.915606  7.915987  9.931175  9.526343  8.978548
##  [8]  9.294965  9.411651  9.338257  9.313521  9.330499  9.335714  9.331797
## 
## $新潟県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429  4.825600 3.279905
## 45.85714 10.887598 9.310178
## 46.00000  6.935754 5.358315
## 46.14286  4.891133 3.289873
## 46.28571  6.838123 5.200761
## 46.42857  6.389320 4.728680
## 46.57143  5.802316 4.120920
## 46.71429  5.963537 4.199986
## 46.85714  6.021466 4.226811
## 47.00000  5.908117 4.092311
## 47.14286  5.829790 3.985614
## 47.28571  5.790105 3.915934
## 47.42857  5.742990 3.841117
## 47.57143  5.688260 3.759487
## 
## $新潟県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 10.66538 12.21107
## 45.85714 16.84723 18.42465
## 46.00000 12.89546 14.47290
## 46.14286 10.94084 12.54210
## 46.28571 13.02423 14.66159
## 46.42857 12.66337 14.32401
## 46.57143 12.15478 13.83618
## 46.71429 12.62639 14.38994
## 46.85714 12.80184 14.59649
## 47.00000 12.76840 14.58420
## 47.14286 12.79725 14.64143
## 47.28571 12.87089 14.74506
## 47.42857 12.92844 14.83031
## 47.57143 12.97533 14.90411
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 2.341932 2.295180 2.250756 2.208542 2.168430 2.130314 2.094096 2.059680
##  [9] 2.026978 1.995903 1.966375 1.938317 1.911655 1.886321
## 
## $富山県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.1115208 -1.410300
## 45.85714 -0.3136079 -1.694617
## 46.00000 -0.4907379 -1.941997
## 46.14286 -0.6474818 -2.159370
## 46.28571 -0.7871963 -2.351810
## 46.42857 -0.9124455 -2.523185
## 46.57143 -1.0252487 -2.676530
## 46.71429 -1.1272340 -2.814284
## 46.85714 -1.2197390 -2.938447
## 47.00000 -1.3038799 -3.050679
## 47.14286 -1.3805999 -3.152381
## 47.28571 -1.4507045 -3.244744
## 47.42857 -1.5148875 -3.328790
## 47.57143 -1.5737515 -3.405403
## 
## $富山県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 4.795385 6.094165
## 45.85714 4.903969 6.284978
## 46.00000 4.992249 6.443508
## 46.14286 5.064566 6.576454
## 46.28571 5.124056 6.688670
## 46.42857 5.173074 6.783813
## 46.57143 5.213440 6.864721
## 46.71429 5.246594 6.933644
## 46.85714 5.273694 6.992402
## 47.00000 5.295686 7.042485
## 47.14286 5.313350 7.085131
## 47.28571 5.327338 7.121377
## 47.42857 5.338198 7.152100
## 47.57143 5.346393 7.178045
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.300193 1.300193 1.300193 1.300193 1.300193 1.300193 1.300193 1.300193
##  [9] 1.300193 1.300193 1.300193 1.300193 1.300193 1.300193
## 
## $石川県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429 -2.340333 -4.267511
## 45.85714 -2.544798 -4.580214
## 46.00000 -2.738927 -4.877107
## 46.14286 -2.924143 -5.160371
## 46.28571 -3.101573 -5.431727
## 46.42857 -3.272123 -5.692561
## 46.57143 -3.436536 -5.944009
## 46.71429 -3.595431 -6.187017
## 46.85714 -3.749328 -6.422382
## 47.00000 -3.898671 -6.650784
## 47.14286 -4.043843 -6.872804
## 47.28571 -4.185174 -7.088951
## 47.42857 -4.322953 -7.299667
## 47.57143 -4.457437 -7.505342
## 
## $石川県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 4.940718  6.867896
## 45.85714 5.145184  7.180599
## 46.00000 5.339312  7.477493
## 46.14286 5.524529  7.760757
## 46.28571 5.701958  8.032113
## 46.42857 5.872508  8.292946
## 46.57143 6.036921  8.544394
## 46.71429 6.195816  8.787403
## 46.85714 6.349713  9.022768
## 47.00000 6.499057  9.251169
## 47.14286 6.644228  9.473190
## 47.28571 6.785559  9.689337
## 47.42857 6.923339  9.900053
## 47.57143 7.057823 10.105728
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.589405 1.430583 1.360932 1.301617 1.251104 1.208087 1.171454 1.140257
##  [9] 1.113689 1.091065 1.071797 1.055389 1.041416 1.029516
## 
## $福井県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.4244666 -1.490546
## 45.85714 -0.7209374 -1.859884
## 46.00000 -1.0104689 -2.265813
## 46.14286 -1.2172679 -2.550685
## 46.28571 -1.3695527 -2.756845
## 46.42857 -1.4839715 -2.909061
## 46.57143 -1.5712246 -3.023111
## 46.71429 -1.6385556 -3.109570
## 46.85714 -1.6910372 -3.175770
## 47.00000 -1.7323069 -3.226909
## 47.14286 -1.7650194 -3.266739
## 47.28571 -1.7911385 -3.297999
## 47.42857 -1.8121337 -3.322712
## 47.57143 -1.8291151 -3.342383
## 
## $福井県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 3.603276 4.669356
## 45.85714 3.582104 4.721051
## 46.00000 3.732333 4.987677
## 46.14286 3.820502 5.153919
## 46.28571 3.871761 5.259053
## 46.42857 3.900146 5.325236
## 46.57143 3.914132 5.366019
## 46.71429 3.919069 5.390084
## 46.85714 3.918416 5.403149
## 47.00000 3.914436 5.409039
## 47.14286 3.908614 5.410334
## 47.28571 3.901917 5.408777
## 47.42857 3.894965 5.405543
## 47.57143 3.888147 5.401415
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 4.819416 5.082925 5.167663 5.194914 5.203677 5.206495 5.207401 5.207693
##  [9] 5.207786 5.207817 5.207826 5.207829 5.207830 5.207831
## 
## $山梨県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.950978 1.961886
## 45.85714 3.020308 1.928424
## 46.00000 3.031996 1.901442
## 46.14286 3.012162 1.856684
## 46.28571 2.981292 1.804833
## 46.42857 2.947113 1.751069
## 46.57143 2.912223 1.697229
## 46.71429 2.877458 1.643906
## 46.85714 2.843075 1.591272
## 47.00000 2.809143 1.539361
## 47.14286 2.775670 1.488165
## 47.28571 2.742648 1.437660
## 47.42857 2.710061 1.387822
## 47.57143 2.677893 1.338625
## 
## $山梨県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 6.687855 7.676947
## 45.85714 7.145541 8.237425
## 46.00000 7.303331 8.433885
## 46.14286 7.377665 8.533144
## 46.28571 7.426062 8.602521
## 46.42857 7.465877 8.661922
## 46.57143 7.502580 8.717574
## 46.71429 7.537928 8.771480
## 46.85714 7.572498 8.824301
## 47.00000 7.606491 8.876272
## 47.14286 7.639982 8.927488
## 47.28571 7.673011 8.977999
## 47.42857 7.705600 9.027839
## 47.57143 7.737768 9.077036
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 22.23879 23.10917 22.40934 21.06629 19.86053 19.19164 19.08721 19.34906
##  [9] 19.72355 20.02115 20.16135 20.15728 20.07125 19.97044
## 
## $長野県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 19.04987 17.36177
## 45.85714 19.79637 18.04268
## 46.00000 19.00651 17.20516
## 46.14286 17.50780 15.62405
## 46.28571 16.02685 13.99742
## 46.42857 14.98324 12.75545
## 46.57143 14.47619 12.03526
## 46.71429 14.36831 11.73166
## 46.85714 14.42894 11.62613
## 47.00000 14.46281 11.52041
## 47.14286 14.37244 11.30797
## 47.28571 14.15432 10.97654
## 47.42857 13.85947 10.57115
## 47.57143 13.54992 10.15110
## 
## $長野県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 25.42770 27.11581
## 45.85714 26.42197 28.17566
## 46.00000 25.81217 27.61352
## 46.14286 24.62478 26.50854
## 46.28571 23.69420 25.72363
## 46.42857 23.40003 25.62783
## 46.57143 23.69823 26.13916
## 46.71429 24.32982 26.96647
## 46.85714 25.01817 27.82097
## 47.00000 25.57949 28.52190
## 47.14286 25.95027 29.01474
## 47.28571 26.16024 29.33802
## 47.42857 26.28304 29.57135
## 47.57143 26.39096 29.78978
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 12.84420 13.40974 15.16554 15.02107 14.43726 16.01414 12.60598 13.67669
##  [9] 13.93161 13.99231 14.00676 14.01020 14.01102 14.01121
## 
## $岐阜県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429  9.270162 7.378178
## 45.85714  9.385203 7.254741
## 46.00000 10.891222 8.628537
## 46.14286 10.541026 8.169435
## 46.28571  9.767148 7.294943
## 46.42857 11.162913 8.594826
## 46.57143  7.580488 4.920155
## 46.71429  8.219626 5.330829
## 46.85714  8.205795 5.174730
## 47.00000  8.036262 4.883322
## 47.14286  7.834942 4.567780
## 47.28571  7.631248 4.254437
## 47.42857  7.431761 3.948913
## 47.57143  7.237642 3.651931
## 
## $岐阜県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 16.41825 18.31023
## 45.85714 17.43428 19.56474
## 46.00000 19.43985 21.70253
## 46.14286 19.50111 21.87270
## 46.28571 19.10736 21.57957
## 46.42857 20.86538 23.43346
## 46.57143 17.63146 20.29180
## 46.71429 19.13376 22.02256
## 46.85714 19.65743 22.68850
## 47.00000 19.94835 23.10129
## 47.14286 20.17857 23.44573
## 47.28571 20.38915 23.76596
## 47.42857 20.59027 24.07312
## 47.57143 20.78478 24.37049
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 38.31682 45.91869 69.17892 62.78795 60.43897 60.14942 53.85001 54.16457
##  [9] 55.99131 55.90142 55.97824 55.91258 55.96870 55.92074
## 
## $静岡県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 31.75219 28.27709
## 45.85714 38.21886 34.14282
## 46.00000 60.95659 56.60395
## 46.14286 54.29243 49.79517
## 46.28571 51.50148 46.77026
## 46.42857 50.93795 46.06169
## 46.57143 44.25199 39.17111
## 46.71429 43.45921 37.79213
## 46.85714 44.51456 38.43913
## 47.00000 43.92564 37.58605
## 47.14286 43.53143 36.94248
## 47.28571 43.00532 36.17262
## 47.42857 42.62231 35.55715
## 47.57143 42.14470 34.85210
## 
## $静岡県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 44.88146 48.35656
## 45.85714 53.61851 57.69456
## 46.00000 77.40124 81.75388
## 46.14286 71.28348 75.78074
## 46.28571 69.37645 74.10767
## 46.42857 69.36090 74.23716
## 46.57143 63.44802 68.52890
## 46.71429 64.86993 70.53701
## 46.85714 67.46806 73.54348
## 47.00000 67.87719 74.21679
## 47.14286 68.42506 75.01400
## 47.28571 68.81985 75.65254
## 47.42857 69.31509 76.38025
## 47.57143 69.69678 76.98938
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 133.8749 173.5522 176.7731 222.0320 212.2275 217.4501 178.1682 172.2225
##  [9] 195.4987 197.3873 223.9357 218.1845 221.2480 198.2058
## 
## $愛知県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 117.4503 108.7556
## 45.85714 153.8593 143.4344
## 46.00000 153.3147 140.8966
## 46.14286 195.6324 181.6573
## 46.28571 183.0924 167.6692
## 46.42857 185.8435 169.1120
## 46.57143 144.2609 126.3114
## 46.71429 131.8559 110.4872
## 46.85714 151.0343 127.4963
## 47.00000 148.7349 122.9799
## 47.14286 171.5676 143.8456
## 47.28571 162.3034 132.7217
## 47.42857 162.0761 130.7524
## 47.57143 135.9123 102.9360
## 
## $愛知県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 150.2995 158.9942
## 45.85714 193.2452 203.6700
## 46.00000 200.2315 212.6496
## 46.14286 248.4317 262.4068
## 46.28571 241.3627 256.7859
## 46.42857 249.0568 265.7883
## 46.57143 212.0755 230.0250
## 46.71429 212.5890 233.9578
## 46.85714 239.9632 263.5012
## 47.00000 246.0397 271.7948
## 47.14286 276.3039 304.0259
## 47.28571 274.0656 303.6473
## 47.42857 280.4199 311.7436
## 47.57143 260.4994 293.4757
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 16.81252 17.37826 18.32308 18.71258 18.42465 18.78700 18.12752 18.32902
##  [9] 18.32902 18.32902 18.32902 18.32902 18.32902 18.32902
## 
## $三重県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 12.90107 10.830479
## 45.85714 13.11345 10.855797
## 46.00000 13.73203 11.301671
## 46.14286 13.81698 11.225401
## 46.28571 13.24236 10.499026
## 46.42857 13.33307 10.445939
## 46.57143 12.41486  9.390758
## 46.71429 12.25113  9.033687
## 46.85714 11.97204  8.606867
## 47.00000 11.70471  8.198013
## 47.14286 11.44775  7.805032
## 47.28571 11.20005  7.426205
## 47.42857 10.96067  7.060107
## 47.57143 10.72883  6.705533
## 
## $三重県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 20.72397 22.79456
## 45.85714 21.64307 23.90072
## 46.00000 22.91413 25.34449
## 46.14286 23.60819 26.19977
## 46.28571 23.60695 26.35028
## 46.42857 24.24093 27.12806
## 46.57143 23.84018 26.86428
## 46.71429 24.40690 27.62434
## 46.85714 24.68599 28.05116
## 47.00000 24.95332 28.46002
## 47.14286 25.21028 28.85300
## 47.28571 25.45798 29.23183
## 47.42857 25.69736 29.59792
## 47.57143 25.92920 29.95250
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 8.924175 7.477253 8.686173 8.270749 8.644543 8.526470 8.642512 8.609393
##  [9] 8.645576 8.636449 8.647786 8.645332 8.648902 8.648266
## 
## $滋賀県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 5.024574 2.9602501
## 45.85714 3.506945 1.4051909
## 46.00000 4.297411 1.9741413
## 46.14286 3.791260 1.4199612
## 46.28571 4.002345 1.5449141
## 46.42857 3.794618 1.2897275
## 46.57143 3.804591 1.2435511
## 46.71429 3.685948 1.0796344
## 46.85714 3.633399 0.9801134
## 47.00000 3.542401 0.8457753
## 47.14286 3.471948 0.7320243
## 47.28571 3.390574 0.6088732
## 47.42857 3.316082 0.4930577
## 47.57143 3.238998 0.3755048
## 
## $滋賀県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 12.82378 14.88810
## 45.85714 11.44756 13.54931
## 46.00000 13.07493 15.39821
## 46.14286 12.75024 15.12154
## 46.28571 13.28674 15.74417
## 46.42857 13.25832 15.76321
## 46.57143 13.48043 16.04147
## 46.71429 13.53284 16.13915
## 46.85714 13.65775 16.31104
## 47.00000 13.73050 16.42712
## 47.14286 13.82362 16.56355
## 47.28571 13.90009 16.68179
## 47.42857 13.98172 16.80475
## 47.57143 14.05753 16.92103
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 21.98052 30.56524 29.63282 24.76787 27.65928 28.90483 26.57343 25.58897
##  [9] 31.94763 30.30532 26.00631 27.41334 29.21102 27.34106
## 
## $京都府$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 15.10927 11.47185
## 45.85714 23.09261 19.13683
## 46.00000 22.01060 17.97564
## 46.14286 16.99895 12.88632
## 46.28571 19.74637 15.55752
## 46.42857 20.85050 16.58680
## 46.57143 18.38013 14.04286
## 46.71429 16.93551 12.35464
## 46.85714 23.05455 18.34684
## 47.00000 21.23528 16.43389
## 47.14286 16.76269 11.86941
## 47.28571 17.99934 13.01587
## 47.42857 19.62968 14.55762
## 47.57143 17.59524 12.43611
## 
## $京都府$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 28.85178 32.48920
## 45.85714 38.03788 41.99365
## 46.00000 37.25504 41.29000
## 46.14286 32.53680 36.64942
## 46.28571 35.57220 39.76104
## 46.42857 36.95916 41.22286
## 46.57143 34.76673 39.10400
## 46.71429 34.24244 38.82330
## 46.85714 40.84071 45.54842
## 47.00000 39.37537 44.17676
## 47.14286 35.24993 40.14321
## 47.28571 36.82735 41.81082
## 47.42857 38.79237 43.86443
## 47.57143 37.08689 42.24601
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 344.6676 501.7815 511.4722 534.1333 560.9193 590.5849 618.6708 481.2947
##  [9] 626.6218 633.2632 668.4089 693.2517 724.2224 765.5573
## 
## $大阪府$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 315.4385 299.9655
## 45.85714 466.7334 448.1801
## 46.00000 469.7228 447.6221
## 46.14286 487.1485 462.2763
## 46.28571 509.0658 481.6162
## 46.42857 534.3324 504.5542
## 46.57143 558.3234 526.3775
## 46.71429 411.4000 374.4001
## 46.85714 550.2720 509.8549
## 47.00000 550.3777 506.5007
## 47.14286 579.6537 532.6695
## 47.28571 598.9323 549.0026
## 47.42857 624.6683 571.9675
## 47.57143 661.0242 605.6876
## 
## $大阪府$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 373.8968 389.3698
## 45.85714 536.8296 555.3829
## 46.00000 553.2216 575.3224
## 46.14286 581.1181 605.9903
## 46.28571 612.7728 640.2224
## 46.42857 646.8373 676.6155
## 46.57143 679.0181 710.9640
## 46.71429 551.1893 588.1892
## 46.85714 702.9715 743.3886
## 47.00000 716.1487 760.0256
## 47.14286 757.1640 804.1482
## 47.28571 787.5711 837.5007
## 47.42857 823.7766 876.4773
## 47.57143 870.0905 925.4270
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 118.6478 157.2734 156.0579 172.8964 171.1882 183.7163 176.1546 160.6363
##  [9] 189.8011 188.9252 201.4206 200.1530 209.4497 203.8384
## 
## $兵庫県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 108.4588 103.0651
## 45.85714 146.2839 140.4664
## 46.00000 143.5959 136.9989
## 46.14286 159.5180 152.4358
## 46.28571 156.9521 149.4160
## 46.42857 168.6714 160.7072
## 46.57143 160.3422 151.9716
## 46.71429 141.7558 131.7610
## 46.85714 169.6406 158.9683
## 47.00000 167.2317 155.7478
## 47.14286 178.4770 166.3315
## 47.28571 176.0241 163.2510
## 47.42857 184.1910 170.8198
## 47.57143 177.4983 163.5547
## 
## $兵庫県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 128.8368 134.2305
## 45.85714 168.2628 174.0803
## 46.00000 168.5199 175.1168
## 46.14286 186.2749 193.3571
## 46.28571 185.4243 192.9605
## 46.42857 198.7612 206.7255
## 46.57143 191.9669 200.3375
## 46.71429 179.5168 189.5116
## 46.85714 209.9617 220.6340
## 47.00000 210.6187 222.1026
## 47.14286 224.3641 236.5097
## 47.28571 224.2819 237.0550
## 47.42857 234.7084 248.0796
## 47.57143 230.1784 244.1220
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 18.48056 18.20480 20.62821 21.20210 19.85435 19.71949 18.61014 19.45142
##  [9] 19.45142 19.45142 19.45142 19.45142 19.45142 19.45142
## 
## $奈良県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 14.18596 11.912538
## 45.85714 13.72668 11.356110
## 46.00000 15.97380 13.509911
## 46.14286 16.37784 13.824034
## 46.28571 14.86603 12.225366
## 46.42857 14.57232 11.847576
## 46.57143 13.30888 10.502565
## 46.71429 13.83510 10.861998
## 46.85714 13.65295 10.583434
## 47.00000 13.47636 10.313358
## 47.14286 13.30484 10.051038
## 47.28571 13.13798  9.795842
## 47.42857 12.97541  9.547219
## 47.57143 12.81683  9.304687
## 
## $奈良県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 22.77516 25.04858
## 45.85714 22.68292 25.05349
## 46.00000 25.28262 27.74651
## 46.14286 26.02635 28.58016
## 46.28571 24.84268 27.48334
## 46.42857 24.86666 27.59141
## 46.57143 23.91139 26.71771
## 46.71429 25.06773 28.04083
## 46.85714 25.24988 28.31940
## 47.00000 25.42647 28.58947
## 47.14286 25.59799 28.85179
## 47.28571 25.76486 29.10699
## 47.42857 25.92742 29.35561
## 47.57143 26.08600 29.59814
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 6.966036 7.992995 7.130029 6.731165 6.874212 7.550363 7.546032 7.568874
##  [9] 7.269984 7.502620 7.298378 7.477692 7.320264 7.458477
## 
## $和歌山県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 4.922288 3.840393
## 45.85714 5.720199 4.517053
## 46.00000 4.785269 3.544028
## 46.14286 4.256193 2.946021
## 46.28571 4.326892 2.978422
## 46.42857 4.889473 3.480883
## 46.57143 4.813164 3.366471
## 46.71429 4.787708 3.315448
## 46.85714 4.439831 2.941638
## 47.00000 4.593966 3.054218
## 47.14286 4.336379 2.768392
## 47.28571 4.443580 2.837418
## 47.42857 4.232784 2.598371
## 47.57143 4.303919 2.633997
## 
## $和歌山県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429  9.009785 10.09168
## 45.85714 10.265791 11.46894
## 46.00000  9.474789 10.71603
## 46.14286  9.206138 10.51631
## 46.28571  9.421533 10.77000
## 46.42857 10.211253 11.61984
## 46.57143 10.278900 11.72559
## 46.71429 10.350040 11.82230
## 46.85714 10.100138 11.59833
## 47.00000 10.411273 11.95102
## 47.14286 10.260377 11.82836
## 47.28571 10.511804 12.11797
## 47.42857 10.407743 12.04216
## 47.57143 10.613035 12.28296
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.1828774 0.2069286 0.2325791 0.2665373 0.2777908 0.2873214 0.2950887
##  [8] 0.2989822 0.3018892 0.3039236 0.3051368 0.3059857 0.3065539 0.3069163
## 
## $鳥取県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.7492257 -1.242651
## 45.85714 -0.7348009 -1.233322
## 46.00000 -0.7200961 -1.224412
## 46.14286 -0.7045080 -1.218548
## 46.28571 -0.6961034 -1.211652
## 46.42857 -0.6888322 -1.205577
## 46.57143 -0.6827865 -1.200442
## 46.71429 -0.6796605 -1.197723
## 46.85714 -0.6773430 -1.195717
## 47.00000 -0.6757591 -1.194372
## 47.14286 -0.6748817 -1.193672
## 47.28571 -0.6743235 -1.193268
## 47.42857 -0.6740134 -1.193094
## 47.57143 -0.6738863 -1.193092
## 
## $鳥取県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 1.114981 1.608406
## 45.85714 1.148658 1.647180
## 46.00000 1.185254 1.689570
## 46.14286 1.237582 1.751623
## 46.28571 1.251685 1.767233
## 46.42857 1.263475 1.780220
## 46.57143 1.272964 1.790620
## 46.71429 1.277625 1.795687
## 46.85714 1.281121 1.799495
## 47.00000 1.283606 1.802219
## 47.14286 1.285155 1.803946
## 47.28571 1.286295 1.805239
## 47.42857 1.287121 1.806202
## 47.57143 1.287719 1.806924
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741
##  [8] 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741 0.4536741
## 
## $島根県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429 -6.241561 -9.785805
## 45.85714 -6.241561 -9.785805
## 46.00000 -6.241561 -9.785805
## 46.14286 -6.241561 -9.785805
## 46.28571 -6.241561 -9.785805
## 46.42857 -6.241561 -9.785805
## 46.57143 -6.241561 -9.785805
## 46.71429 -6.241561 -9.785805
## 46.85714 -6.241561 -9.785805
## 47.00000 -6.241561 -9.785805
## 47.14286 -6.241561 -9.785805
## 47.28571 -6.241561 -9.785805
## 47.42857 -6.241561 -9.785805
## 47.57143 -6.241561 -9.785805
## 
## $島根県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##              80%      95%
## 45.71429 7.14891 10.69315
## 45.85714 7.14891 10.69315
## 46.00000 7.14891 10.69315
## 46.14286 7.14891 10.69315
## 46.28571 7.14891 10.69315
## 46.42857 7.14891 10.69315
## 46.57143 7.14891 10.69315
## 46.71429 7.14891 10.69315
## 46.85714 7.14891 10.69315
## 47.00000 7.14891 10.69315
## 47.14286 7.14891 10.69315
## 47.28571 7.14891 10.69315
## 47.42857 7.14891 10.69315
## 47.57143 7.14891 10.69315
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 12.96619 15.22909 17.23190 15.08568 14.28139 17.18860 13.74149 15.83751
##  [9] 15.32388 13.57532 14.73747 14.97052 13.28909 15.05843
## 
## $岡山県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  9.796982  8.119304
## 45.85714 11.834631 10.037711
## 46.00000 13.742188 11.894846
## 46.14286 11.503254  9.606831
## 46.28571 10.608585  8.664320
## 46.42857 13.427594 11.436636
## 46.57143  9.894292  7.857710
## 46.71429 11.732131  9.558874
## 46.85714 11.086633  8.843573
## 47.00000  9.228692  6.927728
## 47.14286 10.284151  7.926706
## 47.28571 10.413005  8.000400
## 47.42857  8.629702  6.163172
## 47.57143 10.299351  7.780047
## 
## $岡山県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 16.13540 17.81308
## 45.85714 18.62356 20.42048
## 46.00000 20.72161 22.56895
## 46.14286 18.66811 20.56453
## 46.28571 17.95419 19.89846
## 46.42857 20.94962 22.94057
## 46.57143 17.58868 19.62526
## 46.71429 19.94289 22.11615
## 46.85714 19.56112 21.80418
## 47.00000 17.92194 20.22290
## 47.14286 19.19079 21.54824
## 47.28571 19.52804 21.94065
## 47.42857 17.94848 20.41501
## 47.57143 19.81750 22.33681
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 7.413002 7.668956 8.448524 8.182921 7.823815 8.022776 8.175143 8.045977
##  [9] 7.988954 8.065669 8.082175 8.039570 8.038268 8.060590
## 
## $広島県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%        95%
## 45.71429 3.8260465  1.9272271
## 45.85714 3.7408308  1.6614068
## 46.00000 4.3144289  2.1259709
## 46.14286 3.4167188  0.8936437
## 46.28571 2.5099985 -0.3029659
## 46.42857 2.4151291 -0.5533799
## 46.57143 2.2549587 -0.8789975
## 46.71429 1.7408265 -1.5969187
## 46.85714 1.3533830 -2.1592760
## 47.00000 1.1510806 -2.5092816
## 47.14286 0.8790916 -2.9339907
## 47.28571 0.5450653 -3.4222859
## 47.42857 0.2756385 -3.8336496
## 47.57143 0.0434804 -4.2005209
## 
## $広島県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 10.99996 12.89878
## 45.85714 11.59708 13.67651
## 46.00000 12.58262 14.77108
## 46.14286 12.94912 15.47220
## 46.28571 13.13763 15.95059
## 46.42857 13.63042 16.59893
## 46.57143 14.09533 17.22928
## 46.71429 14.35113 17.68887
## 46.85714 14.62452 18.13718
## 47.00000 14.98026 18.64062
## 47.14286 15.28526 19.09834
## 47.28571 15.53407 19.50143
## 47.42857 15.80090 19.91019
## 47.57143 16.07770 20.32170
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 12.76087 13.75679 14.48107 14.85205 20.28394 15.74134 15.43776 14.24311
##  [9] 16.49163 15.21923 17.36903 17.53002 16.69224 15.72840
## 
## $山口県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 10.29987  8.997090
## 45.85714 11.10967  9.708373
## 46.00000 11.71994 10.258283
## 46.14286 11.97853 10.457379
## 46.28571 17.26490 15.666722
## 46.42857 12.64443 11.005019
## 46.57143 12.18327 10.460445
## 46.71429 10.70770  8.836161
## 46.85714 12.71872 10.721456
## 47.00000 11.34672  9.296741
## 47.14286 13.29837 11.143498
## 47.28571 13.35330 11.142283
## 47.42857 12.34814 10.048508
## 47.57143 11.27256  8.913784
## 
## $山口県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 15.22187 16.52465
## 45.85714 16.40391 17.80521
## 46.00000 17.24221 18.70386
## 46.14286 17.72556 19.24671
## 46.28571 23.30298 24.90116
## 46.42857 18.83825 20.47766
## 46.57143 18.69226 20.41508
## 46.71429 17.77853 19.65006
## 46.85714 20.26455 22.26181
## 47.00000 19.09173 21.14171
## 47.14286 21.43968 23.59456
## 47.28571 21.70674 23.91776
## 47.42857 21.03634 23.33596
## 47.57143 20.18424 22.54302
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.7192398 0.9717630 1.1018689 1.1039168 1.2819395 1.0224847 1.0847935
##  [8] 1.0870162 0.8183746 0.9162535 0.9156717 0.8426924 0.8286068 0.9218477
## 
## $徳島県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.9955677 -1.903332
## 45.85714 -0.8257846 -1.777349
## 46.00000 -0.7143233 -1.675757
## 46.14286 -0.7307306 -1.701934
## 46.28571 -0.5709793 -1.551855
## 46.42857 -0.8485270 -1.838981
## 46.57143 -0.8041379 -1.804078
## 46.71429 -0.8954733 -1.944940
## 46.85714 -1.1978290 -2.265143
## 47.00000 -1.1238528 -2.203820
## 47.14286 -1.1480605 -2.240534
## 47.28571 -1.2443982 -2.349237
## 47.42857 -1.2815837 -2.398651
## 47.57143 -1.2111927 -2.340356
## 
## $徳島県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.434047 3.341811
## 45.85714 2.769311 3.720875
## 46.00000 2.918061 3.879495
## 46.14286 2.938564 3.909768
## 46.28571 3.134858 4.115734
## 46.42857 2.893496 3.883950
## 46.57143 2.973725 3.973665
## 46.71429 3.069506 4.118972
## 46.85714 2.834578 3.901892
## 47.00000 2.956360 4.036327
## 47.14286 2.979404 4.071878
## 47.28571 2.929783 4.034622
## 47.42857 2.938797 4.055865
## 47.57143 3.054888 4.184051
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.131427 1.429368 1.429368 1.429368 1.429368 1.429368 1.429368 1.429368
##  [9] 1.429368 1.429368 1.429368 1.429368 1.429368 1.429368
## 
## $香川県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                  80%        95%
## 45.71429 -0.30987363 -1.0728519
## 45.85714 -0.06967529 -0.8632211
## 46.00000 -0.07767663 -0.8754581
## 46.14286 -0.08563571 -0.8876304
## 46.28571 -0.09355320 -0.8997392
## 46.42857 -0.10142973 -0.9117853
## 46.57143 -0.10926595 -0.9237698
## 46.71429 -0.11706246 -0.9356935
## 46.85714 -0.12481985 -0.9475574
## 47.00000 -0.13253872 -0.9593624
## 47.14286 -0.14021963 -0.9711093
## 47.28571 -0.14786314 -0.9827991
## 47.42857 -0.15546978 -0.9944324
## 47.57143 -0.16304009 -1.0060102
## 
## $香川県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.572727 3.335705
## 45.85714 2.928412 3.721958
## 46.00000 2.936413 3.734195
## 46.14286 2.944372 3.746367
## 46.28571 2.952290 3.758476
## 46.42857 2.960166 3.770522
## 46.57143 2.968003 3.782506
## 46.71429 2.975799 3.794430
## 46.85714 2.983557 3.806294
## 47.00000 2.991275 3.818099
## 47.14286 2.998956 3.829846
## 47.28571 3.006600 3.841536
## 47.42857 3.014207 3.853169
## 47.57143 3.021777 3.864747
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 18.242572 14.509921 11.581304  9.283528  7.480705  6.066221  4.956425
##  [8]  4.085684  3.402507  2.866489  2.445933  2.115967  1.857078  1.653954
## 
## $愛媛県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%        95%
## 45.71429 15.9642941 14.7582466
## 45.85714 11.6140977 10.0811413
## 46.00000  8.3637355  6.6604576
## 46.14286  5.8830011  4.0828710
## 46.28571  3.9722930  2.1150517
## 46.42857  2.4930150  0.6014739
## 46.57143  1.3439101 -0.5684395
## 46.71429  0.4491836 -1.4758638
## 46.85714 -0.2486816 -2.1815040
## 47.00000 -0.7937112 -2.7313043
## 47.14286 -1.2198041 -3.1603282
## 47.28571 -1.5531744 -3.4955005
## 47.42857 -1.8141579 -3.7575926
## 47.57143 -2.0185696 -3.9626863
## 
## $愛媛県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429 20.520850 21.726898
## 45.85714 17.405745 18.938701
## 46.00000 14.798873 16.502151
## 46.14286 12.684054 14.484184
## 46.28571 10.989118 12.846359
## 46.42857  9.639427 11.530968
## 46.57143  8.568939 10.481289
## 46.71429  7.722185  9.647233
## 46.85714  7.053695  8.986518
## 47.00000  6.526690  8.464283
## 47.14286  6.111670  8.052194
## 47.28571  5.785108  7.727435
## 47.42857  5.528313  7.471748
## 47.57143  5.326478  7.270595
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 0.3903995 0.3950045 0.3992577 0.4031860 0.4068141 0.4101651 0.4132601
##  [8] 0.4161187 0.4187588 0.4211973 0.4234494 0.4255295 0.4274507 0.4292252
## 
## $高知県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429 -1.291099 -2.181230
## 45.85714 -1.307831 -2.209258
## 46.00000 -1.321571 -2.232522
## 46.14286 -1.332843 -2.251842
## 46.28571 -1.342078 -2.267886
## 46.42857 -1.349625 -2.281202
## 46.57143 -1.355774 -2.292244
## 46.71429 -1.360762 -2.301386
## 46.85714 -1.364789 -2.308942
## 47.00000 -1.368018 -2.315171
## 47.14286 -1.370586 -2.320291
## 47.28571 -1.372607 -2.324484
## 47.42857 -1.374178 -2.327902
## 47.57143 -1.375377 -2.330675
## 
## $高知県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 2.071898 2.962029
## 45.85714 2.097840 2.999267
## 46.00000 2.120086 3.031038
## 46.14286 2.139215 3.058214
## 46.28571 2.155706 3.081514
## 46.42857 2.169955 3.101532
## 46.57143 2.182294 3.118764
## 46.71429 2.193000 3.133624
## 46.85714 2.202306 3.146460
## 47.00000 2.210412 3.157565
## 47.14286 2.217485 3.167190
## 47.28571 2.223667 3.175543
## 47.42857 2.229079 3.182804
## 47.57143 2.233827 3.189125
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 27.17081 29.17231 32.88924 32.02048 36.67197 35.41547 33.90524 32.53095
##  [9] 34.39788 36.95203 36.34033 39.61543 38.73074 37.66739
## 
## $福岡県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%          95%
## 45.71429 13.426120   6.15011965
## 45.85714 12.926405   4.32634050
## 46.00000 14.826776   5.26508415
## 46.14286 12.867098   2.72791223
## 46.28571 16.486548   5.80103297
## 46.42857 14.248272   3.04303461
## 46.57143 11.799821   0.09792035
## 46.71429  8.018757  -4.95721544
## 46.85714  8.274862  -5.55382927
## 47.00000  9.387911  -5.20365185
## 47.14286  7.545510  -7.69754856
## 47.28571  9.640391  -6.22743732
## 47.42857  7.620220  -8.84869291
## 47.57143  5.461403 -11.58741675
## 
## $福岡県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 40.91550 48.19150
## 45.85714 45.41822 54.01829
## 46.00000 50.95171 60.51340
## 46.14286 51.17385 61.31304
## 46.28571 56.85738 67.54290
## 46.42857 56.58267 67.78791
## 46.57143 56.01065 67.71256
## 46.71429 57.04315 70.01912
## 46.85714 60.52090 74.34959
## 47.00000 64.51615 79.10771
## 47.14286 65.13516 80.37822
## 47.28571 69.59047 85.45830
## 47.42857 69.84126 86.31017
## 47.57143 69.87338 86.92219
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.728485 1.728485 1.728485 1.728485 1.728485 1.728485 1.728485 1.728485
##  [9] 1.728485 1.728485 1.728485 1.728485 1.728485 1.728485
## 
## $佐賀県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.2998216 -1.373542
## 45.85714 -0.3723470 -1.484460
## 46.00000 -0.4424510 -1.591675
## 46.14286 -0.5103608 -1.695534
## 46.28571 -0.5762706 -1.796335
## 46.42857 -0.6403472 -1.894331
## 46.57143 -0.7027356 -1.989746
## 46.71429 -0.7635627 -2.082773
## 46.85714 -0.8229400 -2.173583
## 47.00000 -0.8809665 -2.262327
## 47.14286 -0.9377304 -2.349140
## 47.28571 -0.9933108 -2.434143
## 47.42857 -1.0477787 -2.517444
## 47.57143 -1.1011984 -2.599142
## 
## $佐賀県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 3.756792 4.830513
## 45.85714 3.829317 4.941431
## 46.00000 3.899421 5.048646
## 46.14286 3.967331 5.152505
## 46.28571 4.033241 5.253305
## 46.42857 4.097318 5.351302
## 46.57143 4.159706 5.446717
## 46.71429 4.220533 5.539744
## 46.85714 4.279910 5.630553
## 47.00000 4.337937 5.719297
## 47.14286 4.394701 5.806110
## 47.28571 4.450281 5.891113
## 47.42857 4.504749 5.974414
## 47.57143 4.558169 6.056113
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 1.953456 1.556671 1.856342 1.660594 2.130450 2.118667 1.855644 1.930554
##  [9] 1.955179 2.008762 1.969442 2.193815 2.251735 1.967563
## 
## $長崎県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.1629412 -1.283294
## 45.85714 -0.7979511 -2.044413
## 46.00000 -0.9835145 -2.486844
## 46.14286 -1.4639065 -3.117917
## 46.28571 -1.3136115 -3.136787
## 46.42857 -1.5893879 -3.552313
## 46.57143 -2.1122857 -4.212781
## 46.71429 -2.4227663 -4.727275
## 46.85714 -2.6817928 -5.136457
## 47.00000 -2.9287228 -5.542469
## 47.14286 -3.2349723 -5.990023
## 47.28571 -3.2724078 -6.166051
## 47.42857 -3.4604241 -6.484259
## 47.57143 -3.9822847 -7.131944
## 
## $長崎県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 4.069853  5.190206
## 45.85714 3.911294  5.157755
## 46.00000 4.696199  6.199529
## 46.14286 4.785095  6.439106
## 46.28571 5.574511  7.397687
## 46.42857 5.826721  7.789647
## 46.57143 5.823573  7.924068
## 46.71429 6.283875  8.588384
## 46.85714 6.592151  9.046816
## 47.00000 6.946247  9.559993
## 47.14286 7.173856  9.928906
## 47.28571 7.660037 10.553680
## 47.42857 7.963894 10.987729
## 47.57143 7.917411 11.067070
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 3.256844 7.675264 6.342489 6.294766 6.251190 6.211401 6.175069 6.141895
##  [9] 6.111603 6.083944 6.058688 6.035627 6.014569 5.995342
## 
## $熊本県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                  80%        95%
## 45.71429 -1.04285553 -3.3189789
## 45.85714  2.13412642 -0.7991744
## 46.00000  0.70486266 -2.2795162
## 46.14286  0.36736585 -2.7704101
## 46.28571  0.08316104 -3.1819963
## 46.42857 -0.15882568 -3.5310199
## 46.57143 -0.36671297 -3.8297233
## 46.71429 -0.54665057 -4.0873526
## 46.85714 -0.70341268 -4.3110641
## 47.00000 -0.84077680 -4.5065024
## 47.14286 -0.96177742 -4.6781872
## 47.28571 -1.06888220 -4.8297819
## 47.42857 -1.16411839 -4.9642861
## 47.57143 -1.24916582 -5.0841765
## 
## $熊本県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%       95%
## 45.71429  7.556543  9.832667
## 45.85714 13.216401 16.149701
## 46.00000 11.980114 14.964493
## 46.14286 12.222166 15.359942
## 46.28571 12.419219 15.684376
## 46.42857 12.581627 15.953822
## 46.57143 12.716851 16.179862
## 46.71429 12.830440 16.371142
## 46.85714 12.926619 16.534270
## 47.00000 13.008664 16.674390
## 47.14286 13.079153 16.795563
## 47.28571 13.140135 16.901035
## 47.42857 13.193257 16.993425
## 47.57143 13.239849 17.074860
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 6.219915 5.557754 6.670597 4.222164 4.769770 4.489149 4.228882 3.987491
##  [9] 3.763609 3.555964 3.363380 3.184764 3.019103 2.865457
## 
## $大分県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%         95%
## 45.71429 4.5090521  3.60337616
## 45.85714 3.6426810  2.62890266
## 46.00000 4.4549821  3.28210622
## 46.14286 1.8990118  0.66920937
## 46.28571 2.4378873  1.20346333
## 46.42857 2.1093529  0.84956490
## 46.57143 1.8086288  0.52742426
## 46.71429 1.5329709  0.23362622
## 46.85714 1.2799896 -0.03475897
## 47.00000 1.0475844 -0.28027172
## 47.14286 0.8338949 -0.50513368
## 47.28571 0.6372638 -0.71130134
## 47.42857 0.4562074 -0.90050752
## 47.57143 0.2893920 -1.07429429
## 
## $大分県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 7.930777  8.836453
## 45.85714 7.472827  8.486605
## 46.00000 8.886213 10.059088
## 46.14286 6.545316  7.775118
## 46.28571 7.101652  8.336076
## 46.42857 6.868945  8.128733
## 46.57143 6.649135  7.930339
## 46.71429 6.442012  7.741356
## 46.85714 6.247228  7.561977
## 47.00000 6.064344  7.392200
## 47.14286 5.892865  7.231894
## 47.28571 5.732264  7.080829
## 47.42857 5.581998  6.938713
## 47.57143 5.441522  6.805208
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 13.55471 14.35403 14.35403 14.35403 14.35403 14.35403 14.35403 14.35403
##  [9] 14.35403 14.35403 14.35403 14.35403 14.35403 14.35403
## 
## $宮崎県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                80%      95%
## 45.71429 10.731941 9.237658
## 45.85714 11.381435 9.807840
## 46.00000 10.956101 9.157348
## 46.14286 10.578382 8.579677
## 46.28571 10.235157 8.054759
## 46.42857  9.918412 7.570339
## 46.57143  9.622824 7.118277
## 46.71429  9.344648 6.692844
## 46.85714  9.081128 6.289823
## 47.00000  8.830164 5.906008
## 47.14286  8.590117 5.538888
## 47.28571  8.359675 5.186457
## 47.42857  8.137770 4.847083
## 47.57143  7.923518 4.519413
## 
## $宮崎県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 16.37748 17.87176
## 45.85714 17.32662 18.90021
## 46.00000 17.75195 19.55071
## 46.14286 18.12967 20.12838
## 46.28571 18.47290 20.65329
## 46.42857 18.78964 21.13772
## 46.57143 19.08523 21.58978
## 46.71429 19.36341 22.01521
## 46.85714 19.62693 22.41823
## 47.00000 19.87789 22.80205
## 47.14286 20.11794 23.16917
## 47.28571 20.34838 23.52160
## 47.42857 20.57028 23.86097
## 47.57143 20.78454 24.18864
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 3.236982 3.701810 4.152652 4.224634 4.224634 4.224634 4.224634 4.224634
##  [9] 4.224634 4.224634 4.224634 4.224634 4.224634 4.224634
## 
## $鹿児島県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##                 80%       95%
## 45.71429 -0.8450488 -3.005945
## 45.85714 -1.1215612 -3.674900
## 46.00000 -0.8945423 -3.566365
## 46.14286 -0.9162261 -3.637633
## 46.28571 -0.9342025 -3.665125
## 46.42857 -0.9521164 -3.692522
## 46.57143 -0.9699686 -3.719825
## 46.71429 -0.9877597 -3.747034
## 46.85714 -1.0054903 -3.774151
## 47.00000 -1.0231609 -3.801175
## 47.14286 -1.0407722 -3.828110
## 47.28571 -1.0583249 -3.854954
## 47.42857 -1.0758194 -3.881710
## 47.57143 -1.0932563 -3.908377
## 
## $鹿児島県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%       95%
## 45.71429 7.319013  9.479909
## 45.85714 8.525181 11.078519
## 46.00000 9.199845 11.871668
## 46.14286 9.365493 12.086900
## 46.28571 9.383469 12.114392
## 46.42857 9.401383 12.141789
## 46.57143 9.419236 12.169092
## 46.71429 9.437027 12.196301
## 46.85714 9.454757 12.223418
## 47.00000 9.472428 12.250442
## 47.14286 9.490039 12.277377
## 47.28571 9.507592 12.304221
## 47.42857 9.525086 12.330977
## 47.57143 9.542523 12.357644
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##  [1] 35.49991 34.58869 37.72237 41.21550 36.95074 38.57032 35.43005 37.00279
##  [9] 37.00279 37.00279 37.00279 37.00279 37.00279 37.00279
## 
## $沖縄県$lower
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%         95%
## 45.71429 23.58085 17.27127437
## 45.85714 21.62499 14.76242375
## 46.00000 23.79215 16.41794198
## 46.14286 26.38161 18.52902034
## 46.28571 21.26515 12.96170087
## 46.42857 22.07695 13.34589365
## 46.57143 18.16666  9.02797757
## 46.71429 18.10551  8.10188861
## 46.85714 17.11272  6.58354681
## 47.00000 16.16718  5.13747011
## 47.14286 15.26272  3.75422812
## 47.28571 14.39442  2.42627887
## 47.42857 13.55826  1.14747829
## 47.57143 12.75091 -0.08725763
## 
## $沖縄県$upper
## Time Series:
## Start = c(45, 6) 
## End = c(47, 5) 
## Frequency = 7 
##               80%      95%
## 45.71429 47.41897 53.72854
## 45.85714 47.55239 54.41496
## 46.00000 51.65258 59.02679
## 46.14286 56.04939 63.90197
## 46.28571 52.63633 60.93978
## 46.42857 55.06368 63.79474
## 46.57143 52.69343 61.83212
## 46.71429 55.90007 65.90369
## 46.85714 56.89287 67.42203
## 47.00000 57.83840 68.86811
## 47.14286 58.74286 70.25135
## 47.28571 59.61116 71.57930
## 47.42857 60.44732 72.85810
## 47.57143 61.25467 74.09284